Prediction of Abdominal Visceral Obesity From Body Mass Index,Waist Circumference and Waist-hip Ratio in Chinese Adults:Receiver Operating Characteristic Curves Analysis

Objective To evaluate the sensitivity and specificity of body mass index (BMI), waist circumference (WC) and waist-to-hip ratio (WHR) measurements in diagnosing abdominal visceral obesity. Methods BMI, WC, and WHR were assessed in 690 Chinese adults (305 men and 385 women) and compared with magnetic resonance imaging (MRI) measurements of abdominal visceral adipose tissue (VA). Receiver operating characteristic (ROC) curves were generated and used to determine the threshold point for each anthropometric parameter. Results 1) MRI showed that 61.7% of overweight/obese individuals (BMI≥25 kg/m2) and 14.2% of normal weight (BMI<25 kg/m2) individuals had abdominal visceral obesity (VA≥100 cm2). 2) VA was positively correlated with each anthropometric variable, of which WC showed the highest correlation (r=0.73-0.77, P<0.001). 3) The best cut-off points for assessing abdominal visceral obesity were as followed: BMI of 26 kg/m2, WC of 90 cm, and WHR of 0.93, with WC being the most sensitive and specific factor. 4) Among subjects with BMI≥28 kg/m2 or WC≥95 cm, 95% of men and 90% of women appeared to have abdominal visceral obesity. Conclusion Measurements of BMI, WC, and WHR can be used in the prediction of abdominal visceral obesity, of which WC was the one with better accuracy.

Using receiver operating characteristic curves to evaluate the diagnostic value of the combination of multislice spiral CT and alpha-fetoprotein levels for small hepatocellular carcinoma in cirrhotic patients

BACKGROUND： The various combination of multiphase enhancement multislice spiral CT （MSCT） makes the diagno- sis of a small hepatocellular carcinoma （sHCC） on the back- ground of liver cirrhosis possible. This study was to explore whether the combination of MSCT enhancement scan and alpha-fetoprotein （AFP） level ficiency for sHCC. could increase the diagnostic ef- METHODS： This study included 35 sHCC patients and 52 cir- rhotic patients without image evidence of HCC as a control group. The diagnoses were made by three radiologists em- ploying a 5-point rating scale, with postoperative pathologic results as the gold standard. Receiver operating characteristic （ROC） curve analysis was performed to evaluate the diag- nostic value of the three MSCT combination modes （arterial phase＋portal-venous phase, arterial phase＋delayed phase, arterial phase＋portal-venous phase＋delayed phase） and AFP levels for sHCC on the background of liver cirrhosis. RESULTS： The area under ROC curve （AUC）, sensitivity, and specificity of the combination of arterial phase＋portal- venous phase＋delayed phase were 0.93, 93%, and 82%, respectively. The average AUC of the arterial phase＋portal- venous phase＋delayed phase combination was significantly greater than that of the arterial phase＋portal-venous phase （AUC=0.84, P=0.01） and arterial phase＋delayed phase （AUC=0.85, P=0.03）. Arterial phase＋portal-venous phase had a smaller AUC （0.84） than arterial phase＋delayed phase （0.85）, but the difference was insignificant （P=0.15）. After combining MSCT enhancement scan with AFP, the AUC, sensitivity, and specificity were 0.95, 94%, and 83%, respectively, indicating a greatly increased diagnostic efficiency for sHCC. CONCLUSIONS： The combination of AFP and 3 phases MSCT enhancement scan could increase the diagnostic efficiency for sHCC on the background of liver cirrhosis. The application of ROC curve analysis has provided a new method and reference in HCC diagnosis.

Determination of the diagnostic value of the resazurin reduction assay for evaluating boar semen by receiver operating characteristic analysis

Aim： To assess that metabolic status of spermatozoa could provide a useful tool for evaluation of semen quality. Methods： The accuracy of the spectrophotometric application of the resazurin reduction assay was assessed using receiver operating characteristic （ROC） analysis. Results： Areas under ROC curves （AUC） for motile sperm concentration and sperm index （SI） （sperm concentration multiplied by the square root of percentage sperm motility multiplied by the percentage normal sperm morphology） were 0.922. The best discrimination between poor and good semen samples according to the SI was achieved at a cut-off point of A610 = 0.209, where high sensitivity （94.1%） and specificity （91.7%） were calculated. The assay was less accurate when motile sperm concentration was used as the criterion value, yielding sensitivity of 88.2% and specificity of 87.5%, respectively. Likelihood ratios （LR） indicate that absorbances lower than 0.209 were at least 11.3 times as likely to be found in good semen samples than those in poor according to the SI, whereas in the case of motile sperm concentration, the LR was calculated to be 7.06. Conclusion： These results show that the resazurin reduction assay combined with spectrophotometry is an accurate method of assessing the quality of boar semen.

Study on Diagnosis Criteria of Fire-Heat Syndrome Based on Receiver Operating Characteristic Curve and Principal Component Analysis

Objective： To establish the diagnostic quantitative criteria for fire-heat syndrome （FHS） of Chinese medicine （CM） based on the receiver operating characteristic （ROC） curve and principal component analysis （PCA）. Methods： The symptoms and signs of FHS cases and healthy subjects from Guangzhou, Henan and Hunan of China were collected through questionnaire, and the diagnostic quantitative score tables were established for the three regions, respectively, with the method of maximum likelihood analysis. The homogeneity test was then performed on the diagnostic score tables for the three regions with ROC curve, and the diagnostic efficiency of diagnostic score tables for the three regions was compared with the prospective test and retrospective test. The method of PCA was adopted to obtain the analysis matrix for classifying the tapes of FHS. Results： Twenty-seven elements of FHS were confirmed through Chi-square test, and the diagnostic score tables for the three regions were established with the method of maximum likelihood analysis on the basis of the collected case data. According to the ROC curve test, the areas under ROC curve of Guangzhou diagnostic score table assessment with candidates in Guangzhou, Henan and Hunan were 0.998, 0.961 and 0.956, respectively. It showed that the diagnostic efficiency of Guangzhou diagnostic score tables was the highest one. With the prospective test, the area under ROC of Guangzhou diagnostic score table was 0.949, and more than any other diagnostic score table. By PCA, FHS was classified into excess fire and deficiency fire, and then classified into syndrome of flaring up of Heart （Xin） fire, syndrome of Lung （Fei）-Stomach （Wei） excess fire, syndrome of deficiency of Liver （Gan）-yin and Kidney （Shen）-yin, and syndrome of deficiency of Lung-yin from the view of viscera. In the retrospective test, the consistency with clinicians＇ diagnosis was 69.4%, and in the prospective test, it was 70.1%. Conclusions： The Guangzhou diagnostic score table could be used as the recommended criteria for the diagnosis of FHS. The classification of FHS was basically in conformity with the clinical situation.

Receiver operating characteristic curve analysis for image quality in the detection of solitary pulmonary nodules on high resolution paper prints versus dry laser film

Along with the development of digital X-ray imaging technology, a new, economical and practicalpaper-based output system gradually emerged. To reduce cost, more and more radiologic institutions began to change from film copies to lower-priced paper prints for documenting radiologic findings.Therefore, laser paper printers are frequently used as an alternative to duplicating film though the use of printed images is not yet widespread. The aim of this study was to compare the practical value of high-resolution paper printer with dry film copies for detecting small solitary pulmonary nodules （SPNs）.

Determination of the diagnostic value of the resazurin reduction assay for evaluating boar semen by receiver operating cha-racteristic analysis

Aim:To assess that metabolic status of spermatozoa could provide a useful tool for evaluation of semen quality. Methods:The accuracy of the spectrophotometric application of the resazurm reduction assay was assessed using receiver operating characteristic(ROC)analysis.Results:Areas under ROC curves(AUC)for motile sperm concen- tration and sperm index(SI)(sperm concentration multiplied by the square root of percentage sperm motility multi- plied by the percentage normal sperm morphology)were 0.922.The best discrimination between poor and good semen samples according to the SI was achieved at a cut-off point of A_(610)=0.209,where high sensitivity(94.1%)and specificity(91.7%)were calculated.The assay was less accurate when motile sperm concentration was used as the criterion value,vielding sensitivity of 88.2% and specificity of 87.5%,respectively,Likelihood ratios(LR)indicate that absorbances lower than 0.209 were at least 11.3 times as likely to be found in good semen samples than those in poor according to the SI,whereas in the case of motile sperm concentration,the LR was calculated to be 7.06.Conclusion: These results show that the resazurin reduction assay combined with spectrophotometry is an accurate method of assessing the quality of boar semen.

Systemic immune-inflammation index combined with pediatric appendicitis score in assessing the severity and prognosis for paediatric appendicitis

BACKGROUND Pediatric appendicitis is a common cause of abdominal pain in children and is recognized as a significant surgical emergency.A prompt and accurate diagnosis is essential to prevent complications such as perforation and peritonitis.AIM To investigate the predictive value of the systemic immune-inflammation index(SII)combined with the pediatric appendicitis score(PAS)for the assessment of disease severity and surgical outcomes in children aged 5 years and older with appendicitis.METHODS Clinical data of 104 children diagnosed with acute appendicitis were analyzed.The participants were categorized into the acute appendicitis group and chronic appendicitis group based on disease presentation and further stratified into the good prognosis group and poor prognosis group based on prognosis.The SII and PAS were measured,and a joint model using the combined SII and PAS was constructed to predict disease severity and surgical outcomes.RESULTS Significant differences were observed in the SII and PAS parameters between the acute appendicitis group and chronic appendicitis group.Correlation analysis showed associations among the SII,PAS,and disease severity,with the combined SII and PAS model demonstrating significant predictive value for assessing disease severity[aera under the curve(AUC)=0.914]and predicting surgical outcomes(AUC=0.857)in children aged 5 years and older with appendicitis.CONCLUSION The study findings support the potential of integrating the SII with the PAS for assessing disease severity and predicting surgical outcomes in pediatric appendicitis,indicating the clinical utility of the combined SII and PAS model in guiding clinical decision-making and optimizing surgical management strategies for pediatric patients with appendicitis.

The combined detection of carcinoembryonic antigen,carcinogenic antigen 125,and carcinogenic antigen 19-9 in colorectal cancer patients

BACKGROUND Hepatic metastases are common and difficult to treat after colorectal cancer(CRC)surgery.The predictive value of carcinoembryonic antigen(CEA),cancer antigen(CA)125 and CA19-9 combined tests for liver metastasis is unclear.AIM To evaluate predictive value of combined tests for CEA,CA125,and CA19-9 levels in patients with liver metastases of CRC.METHODS The retrospective study included patients with CRC alone(50 cases)and patients with CRC combined with liver metastases(50 cases)who were hospitalized between January 2021 and January 2023.Serum CEA,CA125 and CA19-9 levels were compared between the two groups,and binary logistic regression was used to analyze the predictive value of the combination of these tumor markers in liver metastasis.In addition,we performed receiver operating characteristic(ROC)curve analysis to assess its diagnostic accuracy.RESULTS The results showed that the serum CEA,CA125 and CA19-9 levels in the CRC with liver metastasis group were significantly higher than those in the CRC alone group.Specifically,the average serum CEA level in the CRC with liver metastasis group was 162.03±810.01 ng/mL,while that in the CRC alone group was 5.71±9.76 ng/mL;the average serum CA125 levels were 43.47±83.52 U/mL respectively.and 13.5±19.68 U/mL;the average serum CA19-9 levels were 184.46±473.13 U/mL and 26.55±43.96 U/mL respectively.In addition,binary logistic regression analysis showed that CA125 was significant in predicting CRC liver metastasis(P<0.05).ROC curve analysis results showed that the areas under the ROC curves of CEA,CA125 and CA19-9 were 0.607,0.692 and 0.586.CONCLUSION These results suggest that combined detection of these tumor markers may help early detection and intervention of CRC liver metastasis,thereby improving patient prognosis.

Effectiveness of magnetic resonance imaging and spiral computed tomography in the staging and treatment prognosis of colorectal cancer

BACKGROUND Colorectal cancer(CRC)is a prevalent cancer type in clinical settings;its early signs can be difficult to detect,which often results in late-stage diagnoses in many patients.The early detection and diagnosis of CRC are crucial for improving treatment success and patient survival rates.Recently,imaging techniques have been hypothesized to be essential in managing CRC,with magnetic resonance imaging(MRI)and spiral computed tomography(SCT)playing a significant role in enhancing diagnostic and treatment approaches.AIM To explore the effectiveness of MRI and SCT in the preoperative staging of CRC and the prognosis of laparoscopic treatment.METHODS Ninety-five individuals admitted to Zhongshan Hospital Xiamen University underwent MRI and SCT and were diagnosed with CRC.The precision of MRI and SCT for the presurgical classification of CRC was assessed,and pathological staging was used as a reference.Receiver operating characteristic curves were used to evaluate the diagnostic efficacy of blood volume,blood flow,time to peak,permeability surface,blood reflux constant,volume transfer constant,and extracellular extravascular space volume fraction on the prognosis of patients with CRC.RESULTS Pathological biopsies confirmed the following CRC stages:23,23,32,and 17 at T1,T2,T3,and T4,respectively.There were 39 cases at the N0 stage,22 at N1,34 at N2,44 at M0 stage,and 51 at M1.Using pathological findings as the benchmark,the combined use of MRI and SCT for preoperative TNM staging in patients with CRC demonstrated superior sensitivity,specificity,and accuracy compared with either modality alone,with a statistically significant difference in accuracy(P<0.05).Receiver operating characteristic curve analysis revealed the predictive values for laparoscopic treatment prognosis,as indicated by the areas under the curve for blood volume,blood flow,time to peak,and permeability surface,blood reflux constant,volume transfer constant,and extracellular extravascular space volume fraction were 0.750,0.683,0.772,0.761,0.709,0.719,and 0.910,respectively.The corresponding sensitivity and specificity values were also obtained(P<0.05).CONCLUSION MRI with SCT is effective in the clinical diagnosis of patients with CRC and is worthy of clinical promotion.

Nomogram for predicting lymph node metastasis rate of submucosal gastric cancer by analyzing clinicopathological characteristics associated with lymph node metastasis

Background： To combine dinicopathological characteristics associated with lymph node metastasis for submucosal gastric cancer into a nomogram. Methods： We retrospectively analyzed 262 patients with submucosal gastric cancer who underwent D2 gastrectomy between 1996 and 2012. The relationship between lymph node metastasis and clinicopathological features was statistically analyzed. With multivariate logistic regression analysis, we made a nomogram to predict the possibility of lymph node metastasis. Receiver operating characteristic （ROC） analysis was also performed to assess the predictive value of the model. Discrimination and calibration were performed using internal validation. Results： A total number of 48 （18.3%） patients with submucosal gastric cancer have pathologically lymph node metastasis. For submucosal gastric carcinoma, lymph node metastasis was associated with age, tumor location, macroscopic type, size, differentiation, histology, the existence of ulcer and lymphovascular invasion in univariate analysis （all P〈0.05）. The multivariate logistic old, macroscopic type III or mixed, undifferentiated type, regression analysis identified that age _〈50 years and presence of lymphovascular invasion were independent risk factors of lymph node metastasis in submucosal gastric cancer （all P〈0.05）. We constructed a predicting nomogram with all these factors for lymph node metastasis in submucosal gastric cancer with good discrimination [area under the curve （AUC） =0.844]. Internal validation demonstrated a good discrimination power that the actual probability corresponds closely with the predicted probability. Conclusions： We developed a nomogram to predict the rate of lymph node metastasis for submucosal gastric cancer. With good discrimination and internal validation, the nomogram improved individualized predictions for assisting clinicians to make appropriated treatment decision for submucosal gastric cancer patients.

Prediction of Flash Flood Susceptibility of Hilly Terrain Using Deep Neural Network:A Case Study of Vietnam

Flash floods are one of the most dangerous natural disasters,especially in hilly terrain,causing loss of life,property,and infrastructures and sudden disruption of traffic.These types of floods are mostly associated with landslides and erosion of roads within a short time.Most of Vietnamis hilly and mountainous;thus,the problem due to flash flood is severe and requires systematic studies to correctly identify flood susceptible areas for proper landuse planning and traffic management.In this study,three Machine Learning(ML)methods namely Deep Learning Neural Network(DL),Correlation-based FeatureWeighted Naive Bayes(CFWNB),and Adaboost(AB-CFWNB)were used for the development of flash flood susceptibility maps for hilly road section(115 km length)of National Highway(NH)-6 inHoa Binh province,Vietnam.In the proposedmodels,88 past flash flood events were used together with 14 flash floods affecting topographical and geo-environmental factors.The performance of themodels was evaluated using standard statisticalmeasures including Receiver Operating Characteristic(ROC)Curve,Area Under Curve(AUC)and Root Mean Square Error(RMSE).The results revealed that all the models performed well(AUC>0.80)in predicting flash flood susceptibility zones,but the performance of the DL model is the best(AUC:0.972,RMSE:0.352).Therefore,the DL model can be applied to develop an accurate flash flood susceptibility map of hilly terrain which can be used for proper planning and designing of the highways and other infrastructure facilities besides landuse management of the area.

Support vector machine for prediction of siRNA silencing efficacy

In order to assist the design of short interfering ribonucleic acids （siRNA）, 573 non-redundant siRNAs were collected from published literatures and the relationship between siRNAs sequences and RNA interference （RNAi） effect is analyzed by a support vector machine （SVM） based algorithm relied on a basebase correlation （BBC） feature. The results show that the proposed algorithm has the highest area under curve （AUC） value （0. 73） of the receive operating characteristic （ROC） curve and the greatest r value （0. 43） of the Pearson＇s correlation coefficient. This indicates that the proposed algorithm is better than the published algorithms on the collected datasets and that more attention should be paid to the base-base correlation information in future siRNA design.

Comparative study of CEA and CA19-9 in esophageal,gastric and colon cancers individually and in combination(ROC curve analysis)

Objective： To determine the clinical serum levels of carcinoembryonic antigen （CEA） and carbohydrate antigen 19-9 （CA19-9）, individually and in combination, for the diagnosis of 50 healthy subjects and 150 cases of esophageal, gastric, and colon cancers. Methods： The sensitivities of the two markers were compared individually and in combination, with specificity set at 100%. Receiver operating characteristic （ROC） curves were plotted. Results： Serum CEA levels were significantly higher in cancer patients than in the control group. The sensitivity of CEA was determined： in esophageal cancer, sensitivity=28%, negative predictive value （NPV）=61.72%, and AUC=0.742 （SE=0.05）, with a significance level of P〈0.0001; in gastric cancer, sensitivity=30%, NPV=58.82%, and AUC=0.734 （SE=0.0S）, with a significance level of P〈0.0001; in colon cancer, sensitivity=74%, NPV=79.36%, and AUC=0.856 （SE=0.04）, with a significance level of P〈0.0001. The sensitivity of CA19-9 was also evaluated： in esophageal cancer, sensitivity=18%, NPV=54.94%, and AUC=0.573 （SE=0.05）, with a significance level of P=0.2054. In gastric cancer, sensitivity=42%, NPV=63.29%, and AUC=0.679 （SE=0.05）, with a significance level of P〈0.0011. In colon cancer, sensitivity=26%, NPV=57.47%, and AUC=0.S80 （SE=0.05）, with a significance level ofP=0.1670. The following were the sensitivities of CEA/CA19-9 combined： in esophageal cancer, sensitivity=42%, NPV=63.29%, SE=0.078 （95% CI： 0.0159-0.322）; gastric cancer, sensitivity=S8%, NPV=70.42%, SE=0.072 （9$% CI： -0.0866-0.198）; and colon cancer, sensitivity=72%, NPV=78.12%, SE=0.070 （9S% CI： 0.137-0.415）. Conclusion： CEA exhibited the highest sensitivity for colon cancer, and CA19-9 exhibited the highest sensitivity for gastric cancer. Combined analysis indicated an increase in diagnostic sensitivity in esophageal and gastric cancer compared with that in colon cancer.

Clinical value of serum CA19-9 levels in evaluating resectability of pancreatic carcinoma

AIM:To evaluate the clinical value of serum CA19-9 levels in predicting the respectability of pancreatic carcinoma according to receiver operating characteristic(ROC) curve analysis. METHODS:Serum CA19-9 levels were measured in 104 patients with pancreatic cancer which were possible to be resected according to the imaging. ROC curve was plotted for the CA19-9 levels. The point closest to the upper left-hand corner of the graph were chosen as the cut-off point. The sensitivity,specificity,positive and negative predictive values of CA19-9 at this cut-off point were calculated. RESULTS:Resectable pancreatic cancer was detected in 58(55.77%) patients and unresectable pancreatic cancer was detected in 46(44.23%) patients. The area under the ROC curve was 0.918 and 95% CI was 0.843-0.992. The CA19-9 level was 353.15 U/mL,and the sensitivity and specificity of CA19-9 at this cut-off point were 93.1% and 78.3%,respectively. The positive and negative predictive value was 84.38% and 90%,respectively. CONCLUSION:Preoperative serum CA19-9 level is a useful marker for further evaluating the resectability of pancreatic cancer. Obviously increased serum levels of CA19-9(> 353.15 U/mL) can be regarded as an ancillary parameter for unresectable pancreatic cancer.

Reduced hemoglobin and increased C-reactive protein are associated with upper gastrointestinal bleeding

AIM: To investigate the early upper gastrointestinal endoscopy (endoscopy) significantly reduces mortality resulting from upper gastrointestinal (GI) bleeding.

Serum N-glycan markers for diagnosing liver fibrosis induced by hepatitis B virus

BACKGROUND Hepatitis B virus (HBV) infection is the primary cause of hepatitis with chronic HBV infection,which may develop into liver fibrosis,cirrhosis and hepatocellular carcinoma.Detection of early-stage fibrosis related to HBV infection is of great clinical significance to block the progression of liver lesion.Direct liver biopsy is regarded as the gold standard to detect and assess fibrosis;however,this method is invasive and prone to clinical sampling error.In order to address these issues,we attempted to find more convenient and effective serum markers for detecting HBV-induced early-stage liver fibrosis.AIM To investigate serum N-glycan profiling related to HBV-induced liver fibrosis and verify multiparameter diagnostic models related to serum N-glycan changes.METHODS N-glycan profiles from the sera of 432 HBV-infected patients with liver fibrosis were analyzed.Significant changed N-glycan levels (peaks)(P <0.05) in differentfibrosis stages were selected in the modeling group,and multiparameter diagnostic models were established based on changed N-glycan levels by logistic regression analysis.The receiver operating characteristic (ROC) curve analysis was performed to evaluate diagnostic efficacy of N-glycans models.These models were then compared with the aspartate aminotransferase to platelet ratio index (APRI),fibrosis index based on the four factors (FIB-4),glutamyltranspeptidase platelet albumin index (S index),GlycoCirrho-test,and GlycoFibro-test.Furthermore,we combined multiparameter diagnostic models with alanine aminotransferase (ALT) and platelet (PLT) tests and compared their diagnostic power.In addition,the diagnostic accuracy of N-glycan models was also verified in the validation group of patients.RESULTS Multiparameter diagnostic models constructed based on N-glycan peak 1,3,4and 8 could distinguish between different stages of liver fibrosis.The area under ROC curves (AUROCs) of Model A and Model B were 0.890 and 0.752,respectively differentiating fibrosis F0-F1 from F2-F4,and F0-F2 from F3-F4,and surpassing other serum panels.However,AUROC (0.747) in Model C used for the diagnosis of F4 from F0-F3 was lower than AUROC (0.795) in FIB-4.In combination with ALT and PLT,the multiparameter models showed better diagnostic power (AUROC=0.912,0.829,0.885,respectively) when compared with other models.In the validation group,the AUROCs of the three combined models (0.929,0.858,and 0.867,respectively) were still satisfactory.We also applied the combined models to distinguish adjacent fibrosis stages of 432patients (F0-F1/F2/F3/F4),and the AUROCs were 0.917,0.720 and 0.785.CONCLUSION Multiparameter models based on serum N-glycans are effective supplementary markers to distinguish between adjacent fibrosis stages of patients caused by HBV,especially in combination with ALT and PLT.

Prognostic relevance of β-catenin expression in T2-3N0M0 esophageal squamous cell carcinoma

AIM: To study the expression of β-catenin in esophageal squamous cell carcinoma (ESCC) at stage T2-3N0M0 and its relation with the prognosis of ESCC patients. METHODS: Expression of β-catenin in 227 ESCC speci-mens was detected by immunohistochemistry (IHC). A reproducible semi-quantitative method which takes both staining percentage and intensity into account was applied in IHC scoring, and receiver operating char-acteristic curve analysis was used to select the cut-off score for high or low IHC reactivity. Then, correlation of β-catenin expression with clinicopathological features and prognosis of ESCC patients was determined. RESULTS: No significant correlation was observed between β-catenin expression and clinicopathological parameters in terms of gender, age, tumor size, tumor grade, tumor location, depth of invasion and pathologi-cal stage. The Kaplan-Meier survival curve showed that the up-regulated expression of β-catenin indicated a poorer post-operative survival rate of ESCC patients at stage T2-3N0M0 (P = 0.004), especially of those with T3 lesions (P = 0.014) or with stage ⅡB diseases (P = 0.007). Multivariate analysis also confirmed that β-catenin was an independent prognostic factor for the overall survival rate of ESCC patients at stage T2-3N0M0 (relative risk = 1.642, 95% CI: 1.159-2.327, P = 0.005). CONCLUSION: Elevated β-catenin expression level may be an adverse indicator for the prognosis of ESCC patients at stage T2-3N0M0, especially for those with T3 lesions or stage ⅡB diseases.

Serum gastrin-17 concentration for prediction of upper gastrointestinal tract bleeding risk among peptic ulcer patients

BACKGROUND Serum gastrin-17(G-17),pepsinogen I(PGI),and pepsinogen II(PGII)concentrations regulate gastric acid secretion,and hypersecretion of gastric acid increases the risks of peptic ulcer and upper gastrointestinal bleeding.These associations suggest that serum G-17,PGI,and(or)PGII may predict gastrointestinal bleeding risk among peptic ulcer patients.AIM To evaluate the efficacies of serum G-17,PGI,PGII,and PGI/PGII ratio(PGR)for predicting upper gastrointestinal bleeding among peptic ulcer patients.METHODS A total of 199 patients diagnosed with peptic ulcer confirmed by gastroscopy and positivity for Helicobacter pylori by the 14C-urea breath test were recruited,including 107 patients with simple peptic ulcer and 92 cases complicated by upper gastrointestinal bleeding.Serum PGI,PGII,G-17,and PGR were measured by immune methods and compared between bleeding and non-bleeding groups by univariate analysis.The specificity and sensitivity of PGs and G-17 for evaluating upper gastrointestinal bleeding risk were then assessed by constructing receiver operating characteristic(ROC)curves.RESULTS Serum G-17 was significantly higher among peptic ulcer patients with upper gastrointestinal bleeding compared to simple peptic ulcer patients(25.34±14.29 vs 8.84±8.03 pmol/L,t=9.822,P<0.01),whereas serum PGI,PGII,and PGR did not differ significantly between bleeding and non-bleeding groups(all P>0.05).The risk of bleeding was significantly higher among peptic ulcer patients with elevated serum G-17(>15 pmol/L)compared to patients with normal or low serum G-17(73.2%vs 27.4%,χ2=40.72,P<0.01).The area under the ROC curve for serum G-17 was 0.866±0.024,and a cut-off of 9.86 pmol/L yielded 90.2%sensitivity and 68.2%specificity for distinguishing peptic ulcer with and without upper gastrointestinal bleeding.CONCLUSION Serum G-17 is significantly upregulated in peptic ulcer patients and higher levels are predictive of upper gastrointestinal bleeding.Conversely,serum PGI,PGII,and PGR have no predictive value.Further prospective studies are warranted to examine if high G-17 can be used to assess risk of bleeding prior to onset.

Predictive Vegetation Mapping Approach Based on Spectral Data, DEM and Generalized Additive Models

This study aims to provide a predictive vegetation mapping approach based on the spectral data, DEM and Generalized Additive Models (GAMs). GAMs were used as a prediction tool to describe the relationship between vegetation and environmental variables, as well as spectral variables. Based on the fitted GAMs model, probability map of species occurrence was generated and then vegetation type of each grid was defined according to the probability of species occurrence. Deviance analysis was employed to test the goodness of curve fitting and drop contribution calculation was used to evaluate the contribution of each predictor in the fitted GAMs models. Area under curve (AUC) of Receiver Operating Characteristic (ROC) curve was employed to assess the results maps of probability. The results showed that: 1) AUC values of the fitted GAMs models are very high which proves that integrating spectral data and environmental variables based on the GAMs is a feasible way to map the vegetation. 2) Prediction accuracy varies with plant community, and community with dense cover is better predicted than sparse plant community. 3) Both spectral variables and environmental variables play an important role in mapping the vegetation. However, the contribution of the same predictor in the GAMs models for different plant communities is different. 4) Insufficient resolution of spectral data, environmental data and confounding effects of land use and other variables which are not closely related to the environmental conditions are the major causes of imprecision.

Applying deep learning and benchmark machine learning algorithms for landslide susceptibility modelling in Rorachu river basin of Sikkim Himalaya, India

Landslide is considered as one of the most severe threats to human life and property in the hilly areas of the world.The number of landslides and the level of damage across the globe has been increasing over time.Therefore,landslide management is essential to maintain the natural and socio-economic dynamics of the hilly region.Rorachu river basin is one of the most landslide-prone areas of the Sikkim selected for the present study.The prime goal of the study is to prepare landslide susceptibility maps(LSMs)using computer-based advanced machine learning techniques and compare the performance of the models.To properly understand the existing spatial relation with the landslide,twenty factors,including triggering and causative factors,were selected.A deep learning algorithm viz.convolutional neural network model(CNN)and three popular machine learning techniques,i.e.,random forest model(RF),artificial neural network model(ANN),and bagging model,were employed to prepare the LSMs.Two separate datasets including training and validation were designed by randomly taken landslide and nonlandslide points.A ratio of 70:30 was considered for the selection of both training and validation points.Multicollinearity was assessed by tolerance and variance inflation factor,and the role of individual conditioning factors was estimated using information gain ratio.The result reveals that there is no severe multicollinearity among the landslide conditioning factors,and the triggering factor rainfall appeared as the leading cause of the landslide.Based on the final prediction values of each model,LSM was constructed and successfully portioned into five distinct classes,like very low,low,moderate,high,and very high susceptibility.The susceptibility class-wise distribution of landslides shows that more than 90%of the landslide area falls under higher landslide susceptibility grades.The precision of models was examined using the area under the curve(AUC)of the receiver operating characteristics(ROC)curve and statistical methods like root mean square error(RMSE)and mean absolute error(MAE).In both datasets(training and validation),the CNN model achieved the maximum AUC value of 0.903 and 0.939,respectively.The lowest value of RMSE and MAE also reveals the better performance of the CNN model.So,it can be concluded that all the models have performed well,but the CNN model has outperformed the other models in terms of precision.